elasticsearch/docs/reference/aggregations/bucket/composite-aggregation.asciidoc

[[search-aggregations-bucket-composite-aggregation]]
=== Composite Aggregation

experimental[]

A multi-bucket aggregation that creates composite buckets from different sources.

Unlike the other `multi-bucket` aggregation the `composite` aggregation can be used
to paginate **all** buckets from a multi-level aggregation efficiently. This aggregation
provides a way to stream **all** buckets of a specific aggregation similarly to what
<<search-request-scroll, scroll>> does for documents.

The composite buckets are built from the combinations of the
values extracted/created for each document and each combination is considered as
a composite bucket.

//////////////////////////

[source,js]
--------------------------------------------------
PUT /sales
{
    "mappings": {
        "docs": {
            "properties": {
                "product": {
                    "type": "keyword"
                },
                "timestamp": {
                    "type": "date"
                },
                "price": {
                    "type": "long"
                },
                "shop": {
                    "type": "keyword"
                }
            }
        }
    }
}

POST /sales/docs/_bulk?refresh
{"index":{"_id":0}}
{"product": "mad max", "price": "20", "timestamp": "2017-05-09T14:35"}
{"index":{"_id":1}}
{"product": "mad max", "price": "25", "timestamp": "2017-05-09T12:35"}
{"index":{"_id":2}}
{"product": "rocky", "price": "10", "timestamp": "2017-05-08T09:10"}
{"index":{"_id":3}}
{"product": "mad max", "price": "27", "timestamp": "2017-05-10T07:07"}
{"index":{"_id":4}}
{"product": "apocalypse now", "price": "10", "timestamp": "2017-05-11T08:35"}
-------------------------------------------------
// NOTCONSOLE
// TESTSETUP

//////////////////////////

For instance the following document:

```
{
    "keyword": ["foo", "bar"],
    "number": [23, 65, 76]
}
```
\... creates the following composite buckets when `keyword` and `number` are used as values source
for the aggregation:

```
{ "keyword": "foo", "number": 23 }
{ "keyword": "foo", "number": 65 }
{ "keyword": "foo", "number": 76 }
{ "keyword": "bar", "number": 23 }
{ "keyword": "bar", "number": 65 }
{ "keyword": "bar", "number": 76 }
```

==== Values source

The `values` parameter controls the sources that should be used to build the composite buckets.
There are three different types of values source:

===== Terms

The `terms` value source is equivalent to a simple `terms` aggregation.
The values are extracted from a field or a script exactly like the `terms` aggregation.

Example:

[source,js]
--------------------------------------------------
GET /_search
{
    "aggs" : {
        "my_buckets": {
            "composite" : {
                "sources" : [
                    { "product": { "terms" : { "field": "product" } } }
                ]
            }
        }
     }
}
--------------------------------------------------
// CONSOLE

Like the `terms` aggregation it is also possible to use a script to create the values for the composite buckets:

[source,js]
--------------------------------------------------
GET /_search
{
    "aggs" : {
        "my_buckets": {
            "composite" : {
                "sources" : [
                    {
                        "product": {
                            "terms" : {
                                "script" : {
                                    "source": "doc['product'].value",
                                    "lang": "painless"
                                }
                            }
                        }
                    }
                ]
            }
        }
    }
}
--------------------------------------------------
// CONSOLE

===== Histogram

The `histogram` value source can be applied on numeric values to build fixed size
interval over the values. The `interval` parameter defines how the numeric values should be
transformed. For instance an `interval` set to 5 will translate any numeric values to its closest interval,
a value of `101` would be translated to `100` which is the key for the interval between 100 and 105.

Example:

[source,js]
--------------------------------------------------
GET /_search
{
    "aggs" : {
        "my_buckets": {
            "composite" : {
                "sources" : [
                    { "histo": { "histogram" : { "field": "price", "interval": 5 } } }
                ]
            }
        }
    }
}
--------------------------------------------------
// CONSOLE

The values are built from a numeric field or a script that return numerical values:

[source,js]
--------------------------------------------------
GET /_search
{
    "aggs" : {
        "my_buckets": {
            "composite" : {
                "sources" : [
                    {
                        "histo": {
                            "histogram" : {
                                "interval": 5,
                                "script" : {
                                    "source": "doc['price'].value",
                                    "lang": "painless"
                                }
                            }
                        }
                    }
                ]
            }
        }
    }
}
--------------------------------------------------
// CONSOLE


===== Date Histogram

The `date_histogram` is similar to the `histogram` value source except that the interval
is specified by date/time expression:

[source,js]
--------------------------------------------------
GET /_search
{
    "aggs" : {
        "my_buckets": {
            "composite" : {
                "sources" : [
                    { "date": { "date_histogram" : { "field": "timestamp", "interval": "1d" } } }
                ]
            }
        }
    }
}
--------------------------------------------------
// CONSOLE

The example above creates an interval per day and translates all `timestamp` values to the start of its closest intervals.
Available expressions for interval: `year`, `quarter`, `month`, `week`, `day`, `hour`, `minute`, `second`

Time values can also be specified via abbreviations supported by <<time-units,time units>> parsing.
Note that fractional time values are not supported, but you can address this by shifting to another
time unit (e.g., `1.5h` could instead be specified as `90m`).

====== Time Zone

Date-times are stored in Elasticsearch in UTC.  By default, all bucketing and
rounding is also done in UTC. The `time_zone` parameter can be used to indicate
that bucketing should use a different time zone.

Time zones may either be specified as an ISO 8601 UTC offset (e.g. `+01:00` or
`-08:00`)  or as a timezone id, an identifier used in the TZ database like
`America/Los_Angeles`.

===== Mixing different values source

The `sources` parameter accepts an array of values source.
It is possible to mix different values source to create composite buckets.
For example:

[source,js]
--------------------------------------------------
GET /_search
{
    "aggs" : {
        "my_buckets": {
            "composite" : {
                "sources" : [
                    { "date": { "date_histogram": { "field": "timestamp", "interval": "1d" } } },
                    { "product": { "terms": {"field": "product" } } }
                ]
            }
        }
    }
}
--------------------------------------------------
// CONSOLE


This will create composite buckets from the values created by two values source, a `date_histogram` and a `terms`.
Each bucket is composed of two values, one for each value source defined in the aggregation.
Any type of combinations is allowed and the order in the array is preserved
in the composite buckets.

[source,js]
--------------------------------------------------
GET /_search
{
    "aggs" : {
        "my_buckets": {
            "composite" : {
                "sources" : [
                    { "shop": { "terms": {"field": "shop" } } },
                    { "product": { "terms": { "field": "product" } } },
                    { "date": { "date_histogram": { "field": "timestamp", "interval": "1d" } } }
                ]
            }
        }
    }
}
--------------------------------------------------
// CONSOLE

==== Order

By default the composite buckets are sorted by their natural ordering. Values are sorted
in ascending order of their values. When multiple value sources are requested, the ordering is done per value
source, the first value of the composite bucket is compared to the first value of the other composite bucket and if they are equals the
next values in the composite bucket are used for tie-breaking. This means that the composite bucket
 `[foo, 100]` is considered smaller than `[foobar, 0]` because `foo` is considered smaller than `foobar`.
It is possible to define the direction of the sort for each value source by setting `order` to `asc` (default value)
or `desc` (descending order) directly in the value source definition.
For example:

[source,js]
--------------------------------------------------
GET /_search
{
    "aggs" : {
        "my_buckets": {
            "composite" : {
                "sources" : [
                    { "date": { "date_histogram": { "field": "timestamp", "interval": "1d", "order": "desc" } } },
                    { "product": { "terms": {"field": "product", "order": "asc" } } }
                ]
            }
        }
    }
}
--------------------------------------------------
// CONSOLE

\... will sort the composite bucket in descending order when comparing values from the `date_histogram` source
and in ascending order when comparing values from the `terms` source.

==== Size

The `size` parameter can be set to define how many composite buckets should be returned.
Each composite bucket is considered as a single bucket so setting a size of 10 will return the
first 1O composite buckets created from the values source.
The response contains the values for each composite bucket in an array containing the values extracted
from each value source.

==== After

If the number of composite buckets is too high (or unknown) to be returned in a single response
it is possible to split the retrieval in multiple requests.
Since the composite buckets are flat by nature, the requested `size` is exactly the number of composite buckets
that will be returned in the response (assuming that they are at least `size` composite buckets to return).
If all composite buckets should be retrieved it is preferable to use a small size (`100` or `1000` for instance)
and then use the `after` parameter to retrieve the next results.
For example:

[source,js]
--------------------------------------------------
GET /_search
{
    "aggs" : {
        "my_buckets": {
            "composite" : {
                "size": 2,
                "sources" : [
                    { "date": { "date_histogram": { "field": "timestamp", "interval": "1d" } } },
                    { "product": { "terms": {"field": "product" } } }
                ]
            }
        }
    }
}
--------------------------------------------------
// CONSOLE
// TEST[s/_search/_search\?filter_path=aggregations/]

\... returns:

[source,js]
--------------------------------------------------
{
    ...
    "aggregations": {
        "my_buckets": {
            "buckets": [
                {
                    "key": {
                        "date": 1494201600000,
                        "product": "rocky"
                    },
                    "doc_count": 1
                },
                {
                    "key": {     <1>
                        "date": 1494288000000,
                        "product": "mad max"
                    },
                    "doc_count": 2
                }
            ]
        }
    }
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\.//]

<1> The last composite bucket returned by the query.

The `after` parameter can be used to retrieve the composite buckets that are **after**
the last composite buckets returned in a previous round.
For the example below the last bucket is `"key": [1494288000000, "mad max"]` so the next
round of result can be retrieved with:

[source,js]
--------------------------------------------------
GET /_search
{
    "aggs" : {
        "my_buckets": {
            "composite" : {
                "size": 2,
                 "sources" : [
                    { "date": { "date_histogram": { "field": "timestamp", "interval": "1d", "order": "desc" } } },
                    { "product": { "terms": {"field": "product", "order": "asc" } } }
                ],
                "after": { "date": 1494288000000, "product": "mad max" } <1>
            }
        }
    }
}
--------------------------------------------------
// CONSOLE

<1> Should restrict the aggregation to buckets that sort **after** the provided values.

==== Sub-aggregations

Like any `multi-bucket` aggregations the `composite` aggregation can hold sub-aggregations.
These sub-aggregations can be used to compute other buckets or statistics on each composite bucket created by this
parent aggregation.
For instance the following example computes the average value of a field
per composite bucket:

[source,js]
--------------------------------------------------
GET /_search
{
    "aggs" : {
        "my_buckets": {
            "composite" : {
                 "sources" : [
                    { "date": { "date_histogram": { "field": "timestamp", "interval": "1d", "order": "desc" } } },
                    { "product": { "terms": {"field": "product" } } }
                ]
            },
            "aggregations": {
                "the_avg": {
                    "avg": { "field": "price" }
                }
            }
        }
    }
}
--------------------------------------------------
// CONSOLE
// TEST[s/_search/_search\?filter_path=aggregations/]

\... returns:

[source,js]
--------------------------------------------------
{
    ...
    "aggregations": {
        "my_buckets": {
            "buckets": [
                {
                    "key": {
                        "date": 1494460800000,
                        "product": "apocalypse now"
                    },
                    "doc_count": 1,
                    "the_avg": {
                        "value": 10.0
                    }
                },
                {
                    "key": {
                        "date": 1494374400000,
                        "product": "mad max"
                    },
                    "doc_count": 1,
                    "the_avg": {
                        "value": 27.0
                    }
                },
                {
                    "key": {
                        "date": 1494288000000,
                        "product" : "mad max"
                    },
                    "doc_count": 2,
                    "the_avg": {
                        "value": 22.5
                    }
                },
                {
                    "key": {
                        "date": 1494201600000,
                        "product": "rocky"
                    },
                    "doc_count": 1,
                    "the_avg": {
                        "value": 10.0
                    }
                }
            ]
        }
    }
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\.//]

==== Index sorting

By default this aggregation runs on every document that match the query.
Though if the index sort matches the composite sort this aggregation can optimize
the execution and can skip documents that contain composite buckets that would not
be part of the response.

For instance the following aggregations:

[source,js]
--------------------------------------------------
GET /_search
{
    "aggs" : {
        "my_buckets": {
            "composite" : {
                "size": 2,
                "sources" : [
                    { "date": { "date_histogram": { "field": "timestamp", "interval": "1d", "order": "asc" } } },
                    { "product": { "terms": { "field": "product", "order": "asc" } } }
                ]
            }
        }
    }
}
--------------------------------------------------
// CONSOLE

\... is much faster on an index that uses the following sort:

[source,js]
--------------------------------------------------
PUT twitter
{
    "settings" : {
        "index" : {
            "sort.field" : ["timestamp", "product"],
            "sort.order" : ["asc", "asc"]
        }
    },
    "mappings": {
        "sales": {
            "properties": {
                "timestamp": {
                    "type": "date"
                },
                "product": {
                    "type": "keyword"
                }
            }
        }
    }
}
--------------------------------------------------
// CONSOLE

WARNING: The optimization takes effect only if the fields used for sorting are single-valued and follow
the same order as the aggregation (`desc` or `asc`).

If only the aggregation results are needed it is also better to set the size of the query to 0
and `track_total_hits` to false in order to remove other slowing factors:

[source,js]
--------------------------------------------------
GET /_search
{
    "size": 0,
    "track_total_hits": false,
    "aggs" : {
        "my_buckets": {
            "composite" : {
                "size": 2,
                "sources" : [
                    { "date": { "date_histogram": { "field": "timestamp", "interval": "1d" } } },
                    { "product": { "terms": { "field": "product" } } }
                ]
            }
        }
    }
}
--------------------------------------------------
// CONSOLE

See <<index-modules-index-sorting, index sorting>> for more details.